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公开(公告)号：US11983608B2
公开(公告)日：2024-05-14
申请号：US16438476
申请日：2019-06-12
Applicant: International Business Machines Corporation
Inventor： Venkata Sitaramagiridharganesh Ganapavarapu , Kanthi Sarpatwar , Karthikeyan Shanmugam , Roman Vaculin
Abstract: An example operation may include one or more of generating, by a training participant client, a plurality of transaction proposals, each of the plurality of transaction proposals corresponding to a training iteration for machine learning model training related to stochastic gradient descent, the machine learning model training comprising a plurality of training iterations, the transaction proposals comprising a gradient calculation performed by the training participant client, transferring the plurality of transaction proposals to one or more endorser nodes or peers each comprising a verify gradient smart contract, executing, by each of the endorser nodes or peers, the verify gradient smart contract; and providing endorsements corresponding to the plurality of transaction proposals to the training participation client.
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公开(公告)号：US20230401438A1
公开(公告)日：2023-12-14
申请号：US17806188
申请日：2022-06-09
Applicant: International Business Machines Corporation
Inventor： Isha Puri , Amit Dhurandhar , Tejaswini Pedapati , Karthikeyan Shanmugam , Dennis Wei , Kush Raj Varshney
Abstract: A method, a neural network, and a computer program product are provided that provide training of neural networks with continued fractions architectures. The method includes receiving, as input to a neural network, input data and training the input data through a plurality of continued fractions layers of the neural network to generate output data. The input data is provided to each of the continued fractions layers as well as output data from a previous layer. The method further includes outputting, from the neural network, the output data. Each continued fractions layer of the continued fractions layers is configured to calculate one or more linear functions of its respective input and to generate an output that is used as the input for a subsequent continued fractions layer, each continued fractions layer configured to generate an output that is used as the input for a subsequent layer.
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公开(公告)号：US20210342685A1
公开(公告)日：2021-11-04
申请号：US16862480
申请日：2020-04-29
Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor： Amit Dhurandhar , Karthikeyan Shanmugam , Ronny Luss
Abstract: A computer-implemented method, system, and non-transitory computer-readable storage medium for enhancing performance of a first model. The first model is trained with a training data set. A second model receives the training data set associated with the first model. The second model provides the first model with a hardness value associated with prediction of each data point of the training data set. The first model determines a confidence value regarding predicting each data point based on the training data set, and determines a ratio of the hardness value of a prediction of each data point by the second model with respect to the confidence value of the first model. The first model is retrained with a re-weighted training data set when the determined ratio is lower than a value of β.
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公开(公告)号：US20210319353A1
公开(公告)日：2021-10-14
申请号：US16844987
申请日：2020-04-09
Applicant: International Business Machines Corporation
Inventor： Kanthi Sarpatwar , Karthikeyan Shanmugam , Venkata Sitaramagiridharganesh Ganapavarapu , Roman Vaculin
Abstract: An example operation includes one or more of computing, by a data owner node, updated gradients on a loss function based on a batch of private data and previous parameters of a machine learning model associated with a blockchain, encrypting, by the data owner node, update information, recording, by the data owner, the encrypted update information as a new transaction on the blockchain, and providing the update information for an audit.
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公开(公告)号：US20210182358A1
公开(公告)日：2021-06-17
申请号：US16710893
申请日：2019-12-11
Applicant: International Business Machines Corporation
Inventor： Ajil Jalal , Karthikeyan Shanmugam , Bhanukiran Vinzamuri
Abstract: Techniques regarding root cause analyses based on time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise maintenance component that can detect a cause of failure for a mechanical system by employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine a Granger causality between variables from time series data of the mechanical system given a conditioning set.
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Patent Agency Ranking
公开(公告)号：US20240160694A1
公开(公告)日：2024-05-16
申请号：US18491317
申请日：2023-10-20
Applicant: International Business Machines Corporation
Inventor： Ajil Jalal , Karthikeyan Shanmugam , Bhanukiran Vinzamuri
CPC classification number: G06F17/16 , G01M99/005 , G05B23/0281 , G06F11/079 , G06F17/18 , G06N20/00 , H04L41/0631
Abstract: Techniques regarding root cause analyses based on time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise maintenance component that can detect a cause of failure for a mechanical system by employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine a Granger causality between variables from time series data of the mechanical system given a conditioning set.
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公开(公告)号：US11915131B2
公开(公告)日：2024-02-27
申请号：US17101019
申请日：2020-11-23
Applicant: International Business Machines Corporation
Inventor： Kartik Ahuja , Amit Dhurandhar , Karthikeyan Shanmugam , Kush Raj Varshney
CPC classification number: G06N3/08 , G06N3/047 , G06N3/084 , G06N7/00 , G06N20/00 , G06F9/3836 , G06F17/16 , G06F18/214
Abstract: In an approach to improve the efficiency of solving problem instances by utilizing a machine learning model to solve a sequential optimization problem. Embodiments of the present invention receive a sequential optimization problem for solving and utilize a random initialization to solve a first instance of the sequential optimization problem. Embodiments of the present invention learning, by a computing device a machine learning model, based on a previously stored solution to the first instance of the sequential optimization problem. Additionally, embodiments of the present invention generate, by the machine learning model, one or more subsequent approximate solutions to the sequential optimization problem; and output, by a user interface on the computing device, the one or more subsequent approximate solutions to the sequential optimization problem.
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公开(公告)号：US11816178B2
公开(公告)日：2023-11-14
申请号：US17643470
申请日：2021-12-09
Applicant: International Business Machines Corporation
Inventor： Ajil Jalal , Karthikeyan Shanmugam , Bhanukiran Vinzamuri
IPC: G06F15/16 , G06F9/54 , H04L29/06 , G06F17/16 , G01M99/00 , G06N20/00 , G06F11/07 , G06F17/18 , G05B23/02 , H04L41/0631
CPC classification number: G06F17/16 , G01M99/005 , G05B23/0281 , G06F11/079 , G06F17/18 , G06N20/00 , H04L41/0631
Abstract: Techniques regarding root cause analyses based on time series data are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise maintenance component that can detect a cause of failure for a mechanical system by employing a greedy hill climbing process to perform a polynomial number of conditional independence tests to determine a Granger causality between variables from time series data of the mechanical system given a conditioning set.
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公开(公告)号：US20230128111A1
公开(公告)日：2023-04-27
申请号：US17511723
申请日：2021-10-27
Applicant: International Business Machines Corporation
Inventor： Kristjan Herbert Greenewald , Karthikeyan Shanmugam , Dmitriy A. Katz
Abstract: Estimator mechanisms for automated computer causal effect estimation are provided. An input dataset is received that includes an initial set of covariate data. An estimation of the relevance of covariates in the initial set is performed where relevance is to one or more causal effect relationships between a given at least one action and an outcome. Based on results of the execution of the estimation, a subset of the initial set of covariates is determined that are covariates relevant to one or more causal effect relationships. A modified dataset, comprising the subset of relevant covariates and at least a portion of the input dataset is generated. The modified dataset is input to a causal effect estimator that processes the modified dataset to generate causal effect relationship estimates for specifying causal effects between the given set of actions and the outcome.
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公开(公告)号：US20220284305A1
公开(公告)日：2022-09-08
申请号：US17189211
申请日：2021-03-01
Applicant: International Business Machines Corporation
Inventor： Hamid Dadkhahi , Karthikeyan Shanmugam , Jesus Maria Rios Aliaga , Payel Das
Abstract: A black box evaluator is accessed and a surrogate machine learning model that provides estimates for the optimization of categorical values for the black box evaluator is generated, the surrogate machine learning model being based upon observations from previous executions of the black box evaluator. The black box evaluator is optimized by selecting, by an acquisition function executing on a computing device, a new candidate point for the categorical values. The black box evaluator is executed with the new candidate point for the categorical values.
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